Multi-Agent Learning-Based Optimal Task Offloading and UAV Trajectory Planning for AGIN-Power IoT

UAV-based air-ground integrated computing networks (AGIN) have gained significant traction in remote areas for the Power Internet of Things (PIoT). This paper considers an AGIN-PIoT, where computing tasks generated by ground PIoT devices are offloaded to aerial UAVs that perform edge computing. Jointly optimizing task offloading and UAV trajectory poses challenges such as many decision variables, information uncertainty, and long-term queue delay constraints. Due to the limited battery capacity of PIoT devices and UAVs, our objective is to minimize system energy consumption under long-term queue delay constraints by jointly optimizing task offloading, trajectory planning, and computing resource assignment. In light of Lyapunov optimization, we decompose the original challenging optimization problem into two sub-problems: (1) task offloading and UAV trajectory planning and (2) aerial edge resource allocation. Accordingly, we develop a multi-agent deep reinforcement learning-based algorithm called AGIN-MADDPG for the former to achieve the maximum accumulative reward and propose a greedy solution for the latter. Extensive experiments and numerical results demonstrate that our approach can avoid the problem of gradient vanishing and outperforms other benchmark methods in terms of power consumption, task backlog, queue delay, and system throughput.